Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{langdon:2008:CIGPU2,
author = "W. B. Langdon",
title = "Evolving {GeneChip} Correlation Predictors on Parallel
Graphics Hardware",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "4152--4157",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, GPU,
bioinformatics, microarray, performance",
isbn13 = "978-1-4244-1823-7",
file = "EC0881.pdf",
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_CIGPU2.pdf",
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2008_CIGPU2.ps.gz",
doi = "
doi:10.1109/CEC.2008.4631364",
size = "6 pages",
abstract = "A GPU is used to datamine five million correlations
between probes within Affymetrix HG-U133A probesets
across 6685 human tissue samples from NCBI's GEO
database. These concordances are used as machine
learning training data for genetic programming running
on a Linux PC with a RapidMind OpenGL GLSL backend.
GPGPU is used to identify technological factors
influencing High Density Oligonuclotide Arrays (HDONA)
performance. GP suggests mismatch (PM/MM) and
Adenosine/Guanine ratio influence microarray quality.
Initial results hint that Watson-Crick probe self
hybridisation or folding is not important. Under
GPGPGPU an nVidia GeForce 8800 GTX interprets 300
million GP primitives/second (300 MGPops, approx 8
GFLOPS).",
notes = "RapidMind OpenGL Shading Language (GLSL) back end
WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
Genetic Programming entries for William B Langdon